基于二次神经元网络的面波智能压制方法OA

In seismic exploration in complex near-surface areas such as thick deserts,loess plateaus,and pied-mont zones,surface waves often obscure reflection signals because of their strong energy,seriously interfering with the imaging accuracy of subsurface structures and reducing the reliability of subsequent data interpretation.In recent years,machine learning methods have been developed to effectively attenuate surface wave energy in seismic records.However,these networks are mainly composed of linear neurons,leaving considerable room for improvement in data fitting capability.Therefore,this paper proposes a U-Net network with an attention mechanism based on a quadratic neuron architecture,named QAU-Net,to achieve high-precision suppression of surface wave signals in seismic records.First,a large number of low-velocity near-surface models and subsur-face structural models are established,and the elastic wave equation is used to separately forward model surface waves and subsurface reflection signals to construct the training dataset.This separate forward modeling strategy makes the network's learning objectives more explicit and avoids the influence of surface wave suppression er-rors inherent in traditional methods.Then,to improve generalization performance in practical applications,a small number of high-quality labeled field data samples are generated for transfer learning using the surface wave prediction-subtraction method,which has high suppression accuracy.Results from simulation tests and field ap-plications show that QAU-Net has stronger fitting capability than traditional linear neuron networks,and the in-crease in the number of weight parameters does not slow model convergence.Compared with some traditional methods,QAU-Net also demonstrates efficient and high-quality surface wave suppression capability,providing an efficient and reliable new solution for surface wave processing in seismic exploration.